Raising the achievement of immigrant students: Towards a multi-layered framework for enhanced student outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Results of international achievement surveys such as the Programme in International Student Assessment have consistently reported an achievement gap between immigrant and non-immigrant student populations around the world. This paper unpacks this persistent achievement gap by examining key characteristics that influence the performance of first- and second-generation immigrant students as well as the policies and practices that are associated with enhanced educational outcomes. A multi-layered framework is proposed to help policymakers juxtapose key characteristics of their immigrant students’ achievement against individual, family, school, community, and host society characteristics and policies. The discussion also underscores the importance of connecting this multi-layered framework with other important sectors within governments such as those responsible for the economy, health, social protection, and immigration. This paper also examines limitations with current large-scale data sets and the implications for research and policy analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it